For English language, several reseacrchers used transformers ,for instance, [1]} Proposed a fine-tuned technique for the Bidirectional Encoder Representation from Transformer (BERT) with word unigrams, word2vec, and Hatebase have been used as features. Similarity, [2]} Introduced a fine-tuned a BERT based classifier depends on linear SVM trained on character n-gram as a feature. [3]} Proposed a fine-tuned a BERT and LSTM neural network architecture with automatically and manually crafted features were used namely: word embedding, TFIDF, POS sequences, BOW, the length of the longest punctuation sequence, and the sentiment of the tweets features. However, several researchers applied machine and deep learning, for instance,
[4]} Proposed an ensemble technique consist of Convolutional Neural Network, Bidirectional LSTM with attention, and Bidirectional LSTM + Bidirectional GRU. [5]} Presented two approaches namely: bidirectional with GRU and probabilistic model modified sentence offensiveness calculation (MSOC) trained using word2vec embedding.
